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arXiv 2023年

作者： Chi, Xuemin Zeng, Jun Huang, Jihao Liu, Zhitao Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Hybrid Robotics Group the Department of Mechanical Engineering UC Berkeley United States

We present a fast planning architecture called Hamilton-Jacobi-based bidirectional A* (HJBA*) to solve general tight parking scenarios. The algorithm is a two-layer composed of a high-level HJ-based reachability analysis and a lower-level bidirectional A* search algorithm. In high-level reachability analysis, a backward reachable tube (BRT) concerning vehicle dynamics is computed by the HJ analysis and it intersects with a safe set to get a safe reachable set. The safe set is defined by constraints of positive signed distances for obstacles in the environment and computed by solving QP optimization problems offline. For states inside the intersection set, i.e., the safe reachable set, the computed backward reachable tube ensures they are reachable subjected to system dynamics and input bounds, and the safe set guarantees they satisfy parking safety with respect to obstacles in different shapes. For online computation, randomized states are sampled from the safe reachable set, and used as heuristic guide points to be considered in the bidirectional A* search. The bidirectional A* search is paralleled for each randomized state from the safe reachable set. We show that the proposed two-level planning algorithm is able to solve different parking scenarios effectively and computationally fast for typical parking requests. We validate our algorithm through simulations in large-scale randomized parking scenarios and demonstrate it to be able to outperform other state-of-the-art parking planning algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Robust-Tube Model Predictive Control for Piezoelectrically Actuated Fast Mechanical Switches for Hybrid Circuit Breakers

Robust-Tube Model Predictive Control for Piezoelectrically A...

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Artificial Intelligence and Automation Control (AIAC), International Conference on

作者： Yu Chai Chen Yang Huijuan Dong Jie Zhao Yuri A. W. Shardt Bingfeng Ju ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China Department of Automation Engineering Technical University of Ilmenau Ilmenau Germany School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

Safeguarding high voltage direct current (HVDC) systems against direct current faults is an inherently demanding task. A promising solution to tackle this challenge is the application of piezoelectrically actuated fast mechanical switches (PA-FMS). However, mechanical resonance and random vibrations in the travel curves of P A-FMS have a substantial impact on their control accuracy. To tackle this challenge, a robust-tube model predictive control (RTMPC) strategy is developed. RTMPC drives the disturbed system state into the minimal robustly positively invariant (mRPI) set centered on the target and ensures that constraints are satisfied. The error feedback gain is calculated using the proposed linear matrix inequality (LMI) based method to minimize the effect of disturbances on the trajectory. An efficient outer approximation method is used to compute the mRPI set. Simulation results show that the critically damped travel curves have been achieved, along with a reduction of the external disturbances by 98%.

关键词： Vibrations Uncertain systems Uncertainty Simulation Switches High-voltage techniques Predictive models Trajectory Approximation methods Predictive control

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Collision-Aware MPCC Integrated with Neural Augmentation for Quadrotor Trajectory Tracking

Collision-Aware MPCC Integrated with Neural Augmentation for...

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International Conference on Mechatronics (ICM)

作者： Chuanqi Hu Jin Wang Yikai Chen Yonghang Chen Zhi Zheng Jun Meng Guodong Lu Polytechnic Institute Zhejiang University Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China School of Mechanical Engineering Zhejiang University Hangzhou China National Elite Institute of Engineering Chongqing University Chongqing China College of Electrical Engineering Zhejiang University Hangzhou China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This paper presents a trajectory tracking algorithm designed for autonomous quadrotors to navigate complex dy-namic environments that is resilient to collision disturbances. Since it combines Model Predictive Contouring Control (MPCC) with the capabilities of neural networks, our method is named MPCC Integrated with Neural Augmentation (MINA). By equip-ping the quadrotor with PVDF piezoelectric sensors, we achieve real-time perception of collision forces and utilize S4D-Conv Adaptive Net Architecture (SANA) for state computation, a network that integrates the advantages of S4D and convolutional kernels to enhance the accuracy of state predictions. We incorporate the predicted results into the MPCC framework to optimize the thrust output of the quadrotor for the next time step, ensuring precise trajectory tracking. Experimental validation demonstrates the reliability and effectiveness of our algorithm, providing an innovative solution for autonomous flight and trajectory tracking of quadrotors in complex dynamic environments.

关键词： Trajectory tracking Navigation Heuristic algorithms Neural networks Reliability theory Predictive models Prediction algorithms Real-time systems Sensors Quadrotors

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Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science CambridgeCB1 1PT United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory BristolBS16 1QY United Kingdom Hartpury University Department of Animal and Agriculture GloucesterGL19 3BE United Kingdom Aston University College of Engineering and Physical Sciences BirminghamB4 7ET United Kingdom Royal Military College of Canada Department of Mathematics and Computer Science KingstonONK7K 7B4 Canada Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2014 IEEE.

关键词： Artificial intelligence (AI) autonomous vehicles deep leaning edge computing Internet of Vehicles (IoV)

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Toward Dynamic Control of Tendon-Driven Continuum Robots using Clarke Transform

arXiv

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arXiv 2025年

作者： Muhmann, Christian Grassmann, Reinhard M. Bartholdt, Max Burgner-Kahrs, Jessica Continuum Robotics Laboratory Department of Mathematical and Computational Sciences University of Toronto MississaugaONL5L 1C6 Canada Institute of Mechatronic Systems Faculty of Mechanical Engineering Leibniz University Hannover Garbsen30823 Germany

In this paper, we propose a dynamic model and control framework for tendon-driven continuum robots with multiple segments and an arbitrary number of tendons per segment. Our approach leverages the Clarke transform, the Euler-Lagrange formalism, and the piecewise constant curvature assumption to formulate a dynamic model on a two-dimensional manifold embedded in the joint space that inherently satisfies tendon constraints. We present linear controllers that operate directly on this manifold, along with practical methods for preventing negative tendon forces without compromising control fidelity. We validate these approaches in simulation and on a physical prototype with one segment and five tendons, demonstrating accurate dynamic behavior and robust trajectory tracking under real-time conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Linear control systems

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Detecting and Evaluating Medical Hallucinations in Large Vision Language Models

arXiv

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arXiv 2024年

作者： Chen, Jiawei Yang, Dingkang Wu, Tong Jiang, Yue Hou, Xiaolu Li, Mingcheng Wang, Shunli Xiao, Dongling Li, Ke Zhang, Lihua Academy for Engineering and Technology Fudan University Shanghai China Tencent Youtu Lab Shanghai China Cognition and Intelligent Technology Laboratory Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Shanghai China AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

Large Vision Language Models (LVLMs) are increasingly integral to healthcare applications, including medical visual question answering and imaging report generation. While these models inherit the robust capabilities of foundational Large Language Models (LLMs), they also inherit susceptibility to hallucinations-a significant concern in high-stakes medical contexts where the margin for error is minimal. However, currently, there are no dedicated methods or benchmarks for hallucination detection and evaluation in the medical field. To bridge this gap, we introduce Med-HallMark, the first benchmark specifically designed for hallucination detection and evaluation within the medical multimodal domain. This benchmark provides multi-tasking hallucination support, multifaceted hallucination data, and hierarchical hallucination categorization. Furthermore, we propose the MediHall Score, a new medical evaluative metric designed to assess LVLMs' hallucinations through a hierarchical scoring system that considers the severity and type of hallucination, thereby enabling a granular assessment of potential clinical impacts. We also present MediHallDetector, a novel Medical LVLM engineered for precise hallucination detection, which employs multitask training for hallucination detection. Through extensive experimental evaluations, we establish baselines for popular LVLMs using our benchmark. The findings indicate that MediHall Score provides a more nuanced understanding of hallucination impacts compared to traditional metrics and demonstrate the enhanced performance of MediHallDetector. We hope this work can significantly improve the reliability of LVLMs in medical applications. All resources of this work will be released soon. Copyright © 2024, The Authors. All rights reserved.

关键词： Medical applications

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Marker Correspondence Initialization in an IR Motion Capturing System

Marker Correspondence Initialization in an IR Motion Capturi...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： M. M. Kalantari R. Khorrambakht M. R. Dindarloo S. A. Khalilpour H. D. Taghirad P. Cardou Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Mechanical Engineering Robotics Laboratory Laval University Quebec City Canada

ISBN: (纸本)9781665454537

Marker-based motion capturing (MoCap) systems are of great importance among the different visual referencing systems, because of their robustness, speed, and precision. Due to the lack of highly descriptive visual features in captured images in such systems, establishing the correspondences between the multi-view observations of the markers is a major challenge. This problem is even more challenging when there is no preceding or low reliable data on the markers’ positions. Under these conditions, the correspondences have to be found from a large space of possible matches. This paper aims to provide a solution for this correspondence initialization problem in multi-view tracking systems based on graph theory and Girvan-Newman community detection. The validity and robustness of the algorithm are verified via experimental analysis.

关键词： Visualization Mechatronics Tracking Reliability theory Robustness Graph theory Robots

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Motion Planning of Mobile Robot with Non-holonomic Constraints

Motion Planning of Mobile Robot with Non-holonomic Constrain...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Kewen Zhang Weilong Liu Jin Wang Leijie Chen Guodong Lu Key Laboratory of Special Purpose Equipment and Advanced Processing Technology Ministry of Education College of Mechanical Engineering Zhejiang University of Technology Hangzhou China Robotics Institute of Zhejiang University Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China

This paper proposes a modified spatio-temporal A* path planning algorithm for the trajectory planning of mobile robots with non-holonomic constraints. Most path planning algorithms treat mobile robots as to be with holonomic constraints. However, wheeled mobile robots are affected by non-holonomic constraints, which leads to trajectory unreachability. To solve this problem, an improved spatio-temporal A* algorithm based on non-holonomic constraints is given, which considers the efficiency, completeness and smoothness of the path. At the same time, by improving the construction form of collision constraint and adding spatio-temporal constraints to the path, the success rate and solving speed of the path planning algorithm for non-holonomic constrained mobile robot are increased. Finally, simulation experiments are given to indicate that the proposed planning algorithm provides an optimized path, while improving the planning success rate and reducing the computational time.

关键词：

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SmallRhex: A Fast and Highly-Mobile Hexapod Robot

SmallRhex: A Fast and Highly-Mobile Hexapod Robot

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IEEE International Conference on robotics and Biomimetics

作者： Wenhui Wang Wujie Shi Zerui Li Weiheng Zhuang Zheng Zhu Zhenzhong Jia Department of Mechanical and Energy Engineering Southern University of Science and Technology (SUSTech) Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Uni-versities Shenzhen China

ISBN: (纸本)9781665481106

Relying on the unique C-leg structure, RHex robots have good mobility and traffic ability when having relatively simple structures. Based on the existing RHex robots, taking into account the performance and cost, this design develops a small hexapod robot, named smallRhex, with low cost but strong performance. This paper mainly introduces the mechanical structure, robot gaits, simulation, and physical performance tests of smallRhex robots. The hardware is mainly based on the raspberry pie microcomputer and Robomaster motor and accessories. The high power density meets the dual requirements of performance and cost. Then cooperates with 3D printing and sheet metal and machined parts processing to complete the design of the mechanical structure and assembly of the robot. At the control level, raspberry pie directly controls the movement of 6 motors. The gait design includes basic motion gait, which includes straight walking and turning, and other gaits of complex motion: stair climbing, jumping, and high obstacle climbing. They are simulated in Webots. Finally, the performance test and the gait test of the robot are carried out. Then the gait design is further optimized, and the basic design of smallRhex robot is completed.

关键词： Legged locomotion Power system measurements Costs Metals Stairs Microcomputers Turning

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The Stiffness of 3-PRS PM Across Parasitic and Orientational Workspace

The Stiffness of 3-PRS PM Across Parasitic and Orientational...

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ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Hassen Nigatu Li Jihao Keqi Zhu Junhan Zhang Haotian Guo Guodong Lu Doik Kim Robot Perception and Grasp Lab School of Mechanical Engineering Zhejiang University Hangzhou China Robotics Research Center of Yuyao Yuyao Technology Innovation Center Robotics Institute of Zhejiang University Ningbo Shi Zhejiang Province China The State Key Laboratory of Fluid Power and Mechatronic Systems and the Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical Engineering School of Mechanical Engineering Zhejiang University Hangzhou China xR Lab Center for Intelligent and Interactive Robotics Artificial Intelligence and Robot Institute Korea Institute of Science and Technology (KIST) South Korea

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

This study investigates the stiffness characteristics of the Sprint Z3 head, also known as 3- PRS Parallel Kinematics Machines, which are among the most extensively researched and viably successful manipulators for precision machining applications. Despite the wealth of research on these robotic manipulators, no previous work has demonstrated their stiffness performance within the parasitic motion space. Such an undesired motion influences their stiffness properties, as stiffness is configuration-dependent. Addressing this gap, this paper develops a stiffness model that accounts for both the velocity-level parasitic motion space and the regular workspace. Numerical simulations are provided to illustrate the stiffness characteristics of the manipulator across all considered spaces. The results indicate that the stiffness profile within the parasitic motion space is both shallower and the values are smaller when compared to the stiffness distribution across the orientation workspace. This implies that evaluating a manipulator's performance adequately requires assessing its ability to resist external loads during parasitic motion. Therefore, comprehending this aspect is crucial for redesigning components to enhance overall stiffness.

关键词： Simulation Resists Machining Kinematics Manipulators Numerical simulation Magnetic heads

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